SoroushVahidi/scidocs huggingface dataset:
Hierarchical Reinforcement Learning
(1993)

A hierarchical representation of the input-output transition function in a learning system is suggested. The choice of either representing the knowledge in a learning system as a discrete set of input-output pairs or as a continuous input-output transition function is discussed. The conclusion that both representations could be e cient, but at di erent levels is made. The di erence between strategies and actions is de ned. An algorithm for using adaptive critic methods in a two-level reinforcement learning system is presented. Two problems that are faced, the hierarchical credit assignment problem and the equalized state problem are described. Simulations of a one dimensional hierarchical reinforcement learning system is presented.
Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
(2010)

We present a new estimation principle for parameterized statistical models. The idea is to perform nonlinear logistic regression to discriminate between the observed data and some artificially generated noise, using the model log-density function in the regression nonlinearity. We show that this leads to a consistent (convergent) estimator of the parameters, and analyze the asymptotic variance. In particular, the method is shown to directly work for unnormalized models, i.e. models where the density function does not integrate to one. The normalization constant can be estimated just like any other parameter. For a tractable ICA model, we compare the method with other estimation methods that can be used to learn unnormalized models, including score matching, contrastive divergence, and maximum-likelihood where the normalization constant is estimated with importance sampling. Simulations show that noise-contrastive estimation offers the best trade-off between computational and statistical efficiency. The method is then applied to the modeling of natural images: We show that the method can successfully estimate a large-scale two-layer model and a Markov random field.
D-Sempre: Learning Deep Semantic-Preserving Embeddings for User interests-Social Contents Modeling
(2018)

Exponential growth of social media consumption demands eective user interests-social contents modeling for more personalized recommendation and social media summarization. However, due to the heterogeneous nature of social contents, traditional approaches lack the ability of capturing the hidden semantic correlations across these multi-modal data, which leads to semantic gaps between social content understanding and user interests. To eectively bridge the semantic gaps, we propose a novel deep learning framework for user interests-social contents modeling. We rst mine and parse data, i.e. textual content, visual content, social context and social relation, from heterogeneous social media feeds. en, we design a two-branch network to map the social contents and users into a same latent space. Particularly, the network is trained by a largemargin objective that combines a cross-instance distance constraint with a within-instance semantic-preserving constraint in an endto-end manner. At last, a Deep Semantic-Preserving Embedding (D-Sempre) is learned, and the ranking results can be given by calculating distances between social contents and users. To demonstrate the eectiveness of D-Sempre in user interests-social contents modeling, we construct a Twier dataset and conduct extensive experiments on it. As a result, D-Sempre eectively integrates the multimodal data from heterogeneous social media feeds and captures the hidden semantic correlations between users’ interests and social contents.
A fuzzy classification system for prediction of the results of the basketball games
(2010)

Prediction of the sports game results is an interesting topic that has gained attention lately. Mostly there are used stochastical methods of uncertainty description. In this work it is presented a preliminary approach to build a fuzzy model to basketball game results prediction. Ten fuzzy rule learning algorithms are selected, conducted and compared against standard linear regression with use of the KEEL system. Feature selection algorithms are applied and a majority voting is used in order to select the most representative features.
Inpainting by Flexible Haar-Wavelet Shrinkage
(2008)

We present novel wavelet-based inpainting algorithms. Applying ideas from anisotropic regularization and diffusion, our models can better handle degraded pixels at edges. We interpret our algorithms within the framework of forward-backward splitting methods in convex analysis and prove that the conditions for ensuring their convergence are fulfilled. Numerical examples illustrate the good performance of our algorithms.
Obfuscation procedure based in dead code insertion into crypter
(2016)

The threat that attacks cyberspace is known as malware. In order to infect the technologic devices that are attacked, malware needs to evade the different antivirus systems. To avoid detection, an obfuscation technique must be applied so malware is updated and ready to be performed. No obstant, the technique implementation presents difficulties in terms of its required ability, evasion tests and infection functionality that turn outs to be a problem to keep malware updated. Therefore, a procedure is proposed that allows applying AVFUCKER or DSPLIT techniques. The purpose is to optimize the required technical means, reduce the antivirus analysis and malware functionality check times.
GRAPHITE : Polyhedral Analyses and Optimizations for GCC
(2006)

We present a plan to add loop nest optimizations in GCC based on polyhedral representations of loop nests. We advocate a static analysis approach based on a hierarchy of interchangeable abstractions with solvers that range from the exact solvers such as OMEGA, to faster but less precise solvers based on more coarse abstractions. The intermediate representation GRAPHITE1 (GIMPLE Represented as Polyhedra with Interchangeable Envelopes), built on GIMPLE and the natural loops, hosts the high level loop transformations. We base this presentation on the WRaP-IT project developed in the Alchemy group at INRIA Futurs and Paris-Sud University, on the PIPS compiler developed at École des mines de Paris, and on a joint work with several members of the static analysis and polyhedral compilation community in France. The main goal of this project is to bring more high level loop optimizations to GCC: loop fusion, tiling, strip mining, etc. Thanks to the 1This work was partially supported by ACI/APRON. WRaP-IT experience, we know that the polyhedral analyzes and transformations are affordable in a production compiler. A second goal of this project is to experiment with compile time reduction versus attainable precision when replacing operations on polyhedra with faster operations on more abstract domains. However, the use of a too coarse representation for computing might also result in an over approximated solution that cannot be used in subsequent computations. There exists a trade off between speed of the computation and the attainable precision that has not yet been analyzed for real world programs.
Learning and Recognition of Clothing Genres From Full-Body Images
(2018)

According to the theory of clothing design, the genres of clothes can be recognized based on a set of visually differentiable style elements, which exhibit salient features of visual appearance and reflect high-level fashion styles for better describing clothing genres. Instead of using less-discriminative low-level features or ambiguous keywords to identify clothing genres, we proposed a novel approach for automatically classifying clothing genres based on the visually differentiable style elements. A set of style elements, that are crucial for recognizing specific visual styles of clothing genres, were identified based on the clothing design theory. In addition, the corresponding salient visual features of each style element were identified and formulated with variables that can be computationally derived with various computer vision algorithms. To evaluate the performance of our algorithm, a dataset containing 3250 full-body shots crawled from popular online stores was built. Recognition results show that our proposed algorithms achieved promising overall precision, recall, and  ${F}$ -score of 88.76%, 88.53%, and 88.64% for recognizing upperwear genres, and 88.21%, 88.17%, and 88.19% for recognizing lowerwear genres, respectively. The effectiveness of each style element and its visual features on recognizing clothing genres was demonstrated through a set of experiments involving different sets of style elements or features. In summary, our experimental results demonstrate the effectiveness of the proposed method in clothing genre recognition.
Teaching Introductory Artificial Intelligence with Pac-Man
(2010)

The projects that we have developed for UC Berkeley’s introductory artificial intelligence (AI) course teach foundational concepts using the classic video game Pac-Man. There are four project topics: state-space search, multi-agent search, probabilistic inference, and reinforcement learning. Each project requires students to implement general-purpose AI algorithms and then to inject domain knowledge about the PacMan environment using search heuristics, evaluation functions, and feature functions. We have found that the Pac-Man theme adds consistency to the course, as well as tapping in to students’ excitement about video games.
Considering Social and Emotional Artificial Intelligence
(2010)

This paper presents the concepts of social and emotional intelligence as elements of human intelligence that are complementary to the intelligence assessed by the Turing Test. We argue that these elements are gaining importance as human users are increasingly conceptualising machines as social entities. We describe an implementation of Sensitive Artificial Listeners which provides a hands-on example of technology with some emotional and social skills, and discuss first elements of test methodologies for such technology.
Low numeracy and dyscalculia : identification and intervention
(2010)

One important factor in the failure to learn arithmetic in the normal way is an endogenous core deficit in the sense of number. This has been associated with low numeracy in general (e.g. Halberda et al. in Nature 455:665–668, 2008) and with dyscalculia more specifically (e.g. Landerl et al. in Cognition 93:99–125, 2004). Here, we describe straightforward ways of identifying this deficit, and offer some new ways of strengthening the sense of number using learning technologies.
Visualizing LSTM decisions
(2017)

Long Short-Term Memory (LSTM) recurrent neural networks are renowned for being uninterpretable "black boxes". In the medical domain where LSTMs have shown promise, this is specifically concerning because it is imperative to understand the decisions made by machine learning models in such acute situations. This study employs techniques used in the Convolutional Neural Network domain to elucidate the operations that LSTMs perform on time series. The visualization techniques include input saliency by means of occlusion and derivatives, class mode visualization, and temporal outputs. Moreover, we demonstrate that LSTMs appear to extract features similar to those extracted by wavelets. It was found that deriving the inputs for saliency is a poor approximation and occlusion is a better approach. Moreover, analyzing LSTMs on different sets of data provide novel interpretations.
A survey of performance measures to evaluate ego-lane estimation and a novel sensor-independent measure along with its applications
(2017)

Lane estimation plays a central role for Driver Assistance Systems, therefore many approaches have been proposed to measure its performance. However, no commonly agreed metric exists. In this work, we first present a detailed survey of the current measures. Most of them apply pixel-level benchmarks on camera images and require a time-consuming and fault-prone labeling process. Moreover, these metrics cannot be used to assess other sources such as the detected guardrails, curbs or other vehicles. Therefore, we introduce an efficient and sensor-independent metric, which provides an objective and intuitive self-assessment for the entire road estimation process at multiple levels: individual detectors, lane estimation itself, and the target applications (e.g., lane keeping system). Our metric does not require a high labeling effort and can be used both online and offline. By selecting the evaluated points in specific distances, it can be applied to any road model representation. By comparing in 2D vehicle coordinate system, two possibilities exist to generate the ground-truth: the human-driven path or the expensive alternative with DGPS and detailed maps. This paper applies both methods and reveals that the human-driven path also qualifies for this task and it is applicable to scenarios without GPS signal, e.g., tunnel. Although the lateral offset between reference and detection is widely used in the majority of works, this paper shows that another criterion, the angle deviation, is more appropriate. Finally, we compare our metric with other state-of-the-art metrics using real data recordings from different scenarios.
Why and How Investors Use ESG Information : Evidence from a Global Survey
(2017)

Using survey data from a sample of senior investmen professionals from mainstream (i.e. not SRI funds ) investment organizations we provide insights into w hy and how investors use reported environmental, social and governance (ESG) information. The primar y reason survey respondents consider ESG information in investment decisions is because they consider it financially material to investment performance. ESG information is perceived to provid e information primarily about risk rather than a company’s competitive positioning. There is no one siz fits all, with the financial materiality of di fferent ESG issues varying across sectors. Lack of comparab ility due to the lack of reporting standards is the primary impediment to the use of ESG information. M ost frequently, the information is used to screen companies with the most often used method being neg ative screening. However, negative screening is perceived as the least investment beneficial while ful integration into stock valuation and positive screening considered more beneficial. Respondents expect nega tiv screening to be used less in the future, while positive screening and active ownership to be used more.
The trouble with overconfidence.
(2008)

The authors present a reconciliation of 3 distinct ways in which the research literature has defined overconfidence: (a) overestimation of one's actual performance, (b) overplacement of one's performance relative to others, and (c) excessive precision in one's beliefs. Experimental evidence shows that reversals of the first 2 (apparent underconfidence), when they occur, tend to be on different types of tasks. On difficult tasks, people overestimate their actual performances but also mistakenly believe that they are worse than others; on easy tasks, people underestimate their actual performances but mistakenly believe they are better than others. The authors offer a straightforward theory that can explain these inconsistencies. Overprecision appears to be more persistent than either of the other 2 types of overconfidence, but its presence reduces the magnitude of both overestimation and overplacement.
Dynamic XML documents with distribution and replication
(2003)

The advent of XML as a universal exchange format, and of Web services as a basis for distributed computing, has fostered the apparition of a new class of documents: dynamic XML documents. These are XML documents where some data is given explicitly while other parts are given only intensionally by means of embedded calls to web services that can be called to generate the required information. By the sole presence of Web services, dynamic documents already include inherently some form of distributed computation. A higher level of distribution that also allows (fragments of) dynamic documents to be distributed and/or replicated over several sites is highly desirable in today's Web architecture, and in fact is also relevant for regular (non dynamic) documents.The goal of this paper is to study new issues raised by the distribution and replication of dynamic XML data. Our study has originated in the context of the Active XML system [1, 3, 22] but the results are applicable to many other systems supporting dynamic XML data. Starting from a data model and a query language, we describe a complete framework for distributed and replicated dynamic XML documents. We provide a comprehensive cost model for query evaluation and show how it applies to user queries and service calls. Finally, we describe an algorithm that, for a given peer, chooses data and services that the peer should replicate to improve the efficiency of maintaining and querying its dynamic data.
A measurement study of google play
(2014)

Although millions of users download and use third-party Android applications from the Google Play store, little information is known on an aggregated level about these applications. We have built PlayDrone, the first scalable Google Play store crawler, and used it to index and analyze over 1,100,000 applications in the Google Play store on a daily basis, the largest such index of Android applications. PlayDrone leverages various hacking techniques to circumvent Google's roadblocks for indexing Google Play store content, and makes proprietary application sources available, including source code for over 880,000 free applications. We demonstrate the usefulness of PlayDrone in decompiling and analyzing application content by exploring four previously unaddressed issues: the characterization of Google Play application content at large scale and its evolution over time, library usage in applications and its impact on application portability, duplicative application content in Google Play, and the ineffectiveness of OAuth and related service authentication mechanisms resulting in malicious users being able to easily gain unauthorized access to user data and resources on Amazon Web Services and Facebook.
Asymmetrical Duty Cycle-Controlled LLC Resonant Converter With Equivalent Switching Frequency Doubler
(2016)

In the conventional full-bridge LLC converter, the duty cycle is kept as 0.5 and the phase-shift angle of the half-bridge modules is 0° to be a symmetrical operation, which makes the resonant tank operating frequency only equal to the switching frequency of the power devices. By regulating the duty cycles of the upper and lower switches in each half-bridge module to be 0.75 and 0.25 and the phase-shift angle of the half-bridge modules to be 180°, the asymmetrical duty cycle controlled full-bridge LLC resonant converter is derived. The proposed asymmetrical duty cycle controlled scheme halves the switching frequency of the primary switches. As a result, the driving losses are effectively reduced. Compared with the conventional full-bridge LLC converter, the soft-switching condition of the derived asymmetrical controlled LLC converter becomes easier to reduce the resonant current. Consequently, the conduction losses are decreased and the conversion efficiency is improved. The asymmetrical control scheme can be also extended to the stacked structure for high input voltage applications. Finally, two LLC converter prototypes both with 200-kHz resonant frequency for asymmetrical and symmetrical control schemes are built and compared to validate the effectiveness of the proposed control strategy.
Behavioural Analytics using Process Mining in On-line Advertising
(2017)

Online behavioural targeting is one of the most popular business strategies on the display advertising today. It is based primarily on analysing web user behavioural data with the usage of machine learning techniques with the aim to optimise web advertising. Being able to identify “unknown” and “first time seen” customers is of high importance in online advertising since a successful guess could identify “possible prospects” who would be more likely to purchase an advertisement’s product. By identifying prospective customers, online advertisers may be able to optimise campaign performance, maximise their revenue as well as deliver advertisements tailored to a variety of user interests. This work presents a hybrid approach benchmarking machine-learning algorithms and attribute preprocessing techniques in the context of behavioural targeting in process oriented environments. The performance of our suggested methodology is evaluated using the key performance metric in online advertising which is the predicted conversion rate. Our experimental results indicate that the presented process mining framework can significantly identify prospect customers in most cases. Our results seem promising, indicating that there is a need for further workflow research in online display
Design and use paradigms for Gazebo, an open-source multi-robot simulator
(2004)

Simulators have played a critical role in robotics research as tools for quick and efficient testing of new concepts, strategies, and algorithms. To date, most simulators have been restricted to 2D worlds, and few have matured to the point where they are both highly capable and easily adaptable. Gazebo is designed to fill this niche by creating a 3D dynamic multi-robot environment capable of recreating the complex worlds that would be encountered by the next generation of mobile robots. Its open source status, fine grained control, and high fidelity place Gazebo in a unique position to become more than just a stepping stone between the drawing board and real hardware: data visualization, simulation of remote environments, and even reverse engineering of blackbox systems are all possible applications. Gazebo is developed in cooperation with the Player and Stage projects (Gerkey, B. P., et al., July 2003), (Gerkey, B. P., et al., May 2001), (Vaughan, R. T., et al., Oct. 2003), and is available from http://playerstage.sourceforge.net/gazebo/ gazebo.html.
Probabilistic Semi-Dense Mapping from Highly Accurate Feature-Based Monocular SLAM
(2015)

In the last years several direct (i.e. featureless) monocular SLAM approaches have appeared showing impressive semi-dense or dense scene reconstructions. These works have questioned the need of features, in which consolidated SLAM techniques of the last decade were based. In this paper we present a novel feature-based monocular SLAM system that is more robust, gives more accurate camera poses, and obtains comparable or better semi-dense reconstructions than the current state of the art. Our semi-dense mapping operates over keyframes, optimized by local bundle adjustment, allowing to obtain accurate triangulations from wide baselines. Our novel method to search correspondences, the measurement fusion and the inter-keyframe depth consistency tests allow to obtain clean reconstructions with very few outliers. Against the current trend in direct SLAM, our experiments show that by decoupling the semi-dense reconstruction from the trajectory computation, the results obtained are better. This opens the discussion on the benefits of features even if a semi-dense reconstruction is desired.